Import and format data
Import IPUMS
Use of data from IPUMS USA is subject to conditions including that users should
cite the data appropriately. Use command `ipums_conditions()` for more details.
|= | 1% 6 MB
|=== | 3% 13 MB
|===== | 5% 19 MB
|======= | 7% 26 MB
|========= | 9% 32 MB
|=========== | 10% 39 MB
|============= | 12% 45 MB
|=============== | 14% 52 MB
|================= | 16% 58 MB
|=================== | 18% 65 MB
|===================== | 19% 71 MB
|======================= | 21% 78 MB
|========================= | 23% 84 MB
|=========================== | 25% 91 MB
|============================ | 27% 97 MB
|============================== | 28% 104 MB
|================================ | 30% 110 MB
|================================== | 32% 117 MB
|==================================== | 34% 123 MB
|====================================== | 36% 130 MB
|======================================== | 37% 136 MB
|========================================== | 39% 143 MB
|============================================ | 41% 149 MB
|============================================== | 43% 156 MB
|================================================ | 45% 162 MB
|================================================== | 46% 169 MB
|==================================================== | 48% 175 MB
|====================================================== | 50% 182 MB
|======================================================= | 52% 188 MB
|========================================================= | 54% 195 MB
|=========================================================== | 55% 201 MB
|============================================================= | 57% 208 MB
|=============================================================== | 59% 214 MB
|================================================================= | 61% 221 MB
|=================================================================== | 63% 227 MB
|===================================================================== | 64% 234 MB
|======================================================================= | 66% 240 MB
|========================================================================= | 68% 247 MB
|=========================================================================== | 70% 253 MB
|============================================================================= | 72% 260 MB
|=============================================================================== | 73% 266 MB
|================================================================================= | 75% 273 MB
|================================================================================== | 77% 279 MB
|==================================================================================== | 79% 286 MB
|====================================================================================== | 81% 292 MB
|======================================================================================== | 82% 299 MB
|========================================================================================== | 84% 305 MB
|============================================================================================ | 86% 312 MB
|============================================================================================== | 88% 318 MB
|================================================================================================ | 90% 325 MB
|================================================================================================== | 91% 331 MB
|==================================================================================================== | 93% 338 MB
|====================================================================================================== | 95% 344 MB
|======================================================================================================== | 97% 351 MB
|==========================================================================================================| 99% 357 MB
|===========================================================================================================| 100% 360 MB
PUMA shapefiles
OGR data source with driver: ESRI Shapefile
Source: "/Users/chriszs/Desktop/work/covid-immigrant-foodworkers/data/ipums_puma_2010/ipums_puma_2010.shp", layer: "ipums_puma_2010"
with 2378 features
It has 7 fields
Integer64 fields read as strings: GISMATCH
County shapefiles
OGR data source with driver: ESRI Shapefile
Source: "/Users/chriszs/Desktop/work/covid-immigrant-foodworkers/data/tl_2019_us_county/tl_2019_us_county.shp", layer: "tl_2019_us_county"
with 3233 features
It has 17 fields
Integer64 fields read as strings: ALAND AWATER
COVID county cases and deaths
covid <- read_csv("data/us-counties.csv",
col_types = cols(.default = "?", date = "D")) %>%
rename(county_code = fips) %>%
# Filter to the latest data for each county
group_by(county_code) %>%
slice(which.max(date))
covid
County populations
Read-in Census API key
census_key <- (readLines(key_file)[1])
Create list of all US states to iterate through in the API calls
us <- unique(fips_codes$state)[1:51]
us
[1] "AL" "AK" "AZ" "AR" "CA" "CO" "CT" "DE" "DC" "FL" "GA" "HI" "ID" "IL" "IN" "IA" "KS" "KY" "LA" "ME" "MD" "MA" "MI"
[24] "MN" "MS" "MO" "MT" "NE" "NV" "NH" "NJ" "NM" "NY" "NC" "ND" "OH" "OK" "OR" "PA" "RI" "SC" "SD" "TN" "TX" "UT" "VT"
[47] "VA" "WA" "WV" "WI" "WY"
Download population data from the 2014-2018 ACS
PUMA-to-county crosswalk
county_to_puma_crosswalk <- read_csv("data/geocorr2018.csv", skip = 1) %>%
mutate(puma_code = paste0(`State code`, `PUMA (2012)`)) %>%
select(
puma_code,
county_code = `County code (2014)`,
state = `State abbreviation`,
county = `2014 county name`,
puma = `PUMA12 name`,
allocation_factor = `puma12 to county14 allocation factor`
)
county_to_puma_crosswalk
Join the COVID, population and crosswalk data frames
crosswalk <- list(covid, population, county_to_puma_crosswalk) %>%
reduce(full_join, by = "county_code") %>%
mutate(
puma = paste(puma, state.x, sep = ", "),
cases_per_100k = round(cases / population * 100000, digits = 0),
deaths_per_100k = round(deaths / population * 100000, digits = 0)
) %>%
select(
1,
state = state.x,
county_code,
county = county.y,
puma_code,
puma,
population,
cases,
deaths,
cases_per_100k,
deaths_per_100k,
allocation_factor
)
crosswalk
Filter to the food production industries we’re interested in
food_production_industries <-
list("0170", # Crop production
"0180", # Animal production and aquaculture
"0290", # Support activities for agriculture and forestry
"1070", # Animal food, grain and oilseed milling
"1080", # Sugar and confectionery products
"1090", # Fruit and vegetable preserving and specialty food manufacturing
"1170", # Dairy product manufacturing
"1180", # Animal slaughtering and processing
"1270", # Bakeries and tortilla manufacturing, except retail bakeries
"1280", # Seafood and other miscellaneous foods; n.e.c.
"1290") # Not specified food industries
ipums_food_production <- ipums %>%
filter(industry_code %in% food_production_industries)
Calculate employment by industry-occupation grouping
ipums_food_production_employment_by_ind_occ <-
ipums_food_production %>%
group_by(industry, occupation) %>%
summarize(num_employees = sum(PERWT)) %>%
pivot_wider(
id_cols = c(industry, occupation),
values_from = num_employees,
names_prefix = "num_employees"
) %>%
replace(is.na(.), 0) %>%
# Exclude from our analysis ind-occ groupings
# with fewer than 2,500 employees nationally
filter(num_employees >= 2500)
Export the data
write_csv(
ipums_food_production_employment_by_ind_occ,
"data/exported/ipums_food_production_employment_by_ind_occ.csv"
)
Filter the industry-occupation pairs
Import data frame of food production industry-occupation pairs
ipums_food_production_employment_by_ind_occ <-
read_csv("data/ipums_food_production_total_employment_by_ind_occ.csv")
Join the industry-occupation pairs to the ipums data and filter to just the industry-occupation pairs we want
ipums_food_production_selected_occupations <-
ipums_food_production %>%
inner_join(ipums_food_production_employment_by_ind_occ,
by = c("industry", "occupation")) %>%
filter(exclude == FALSE) %>%
select(-num_employees, -exclude)
ipums_food_production_selected_occupations
And join with the pumas data frame to get the PUMA names
ipums_food_production_selected_occupations <-
ipums_food_production_selected_occupations %>%
left_join(pumas, by = "puma_code") %>%
mutate(puma = paste(puma, state, sep = ", ")) %>%
select(1:3,
13,
4:12)
ipums_food_production_selected_occupations
citizenship_lookup = tibble(
citizenship_code = factor(c(
0, 1, 2, 3, 4, 5
)),
citizenship = c(
"native_citizen", # n/a
"native_citizen", # born_abroad_citizen
"naturalized_citizen", # naturalized_citizen
"non_citizen", # non_citizen
"non_citizen_with_papers", # non_citizen_with_papers
"foreign_born_status_unknown"
)
)
ethnicity_lookup = tibble(
ethnicity_code = factor(c(
0, 1, 2, 3, 4, 9
)),
ethnicity = c(
"non_hispanic",
"hispanic", # mexican
"hispanic", # puerto_rican
"hispanic", # cuban
"hispanic", # other
"not_reported"
)
)
ipums_food_production_selected_occupations_mapped <-
ipums_food_production_selected_occupations %>%
select(-ethnicity,-citizenship) %>%
left_join(ethnicity_lookup, by="ethnicity_code") %>%
left_join(citizenship_lookup, by="citizenship_code")
pct <- function (part,whole) {
round(part / whole * 100, digits = 0)
}
calc_percents <- function (rows) {
rows %>%
mutate(
total_native_citizen = (
total_native_citizen_non_hispanic +
total_native_citizen_hispanic
),
total_naturalized_citizen = (
total_naturalized_citizen_non_hispanic +
total_naturalized_citizen_hispanic
),
total_non_citizen = (
total_non_citizen_non_hispanic +
total_non_citizen_hispanic
),
total_foreign_non_hispanic = (
total_naturalized_citizen_non_hispanic +
total_non_citizen_non_hispanic
),
total_foreign_hispanic = (
total_naturalized_citizen_hispanic +
total_non_citizen_hispanic
),
total_foreign = (
total_foreign_non_hispanic +
total_foreign_hispanic
),
total_employees = (
total_native_citizen +
total_naturalized_citizen +
total_non_citizen
),
pct_native_citizen_non_hispanic =
pct(total_native_citizen_non_hispanic, total_employees),
pct_native_citizen_hispanic =
pct(total_native_citizen_hispanic, total_employees),
pct_total_foreign_non_hispanic =
pct(total_foreign_non_hispanic, total_employees),
pct_total_foreign_hispanic =
pct(total_foreign_hispanic, total_employees),
pct_naturalized_citizen_non_hispanic =
pct(total_naturalized_citizen_non_hispanic, total_employees),
pct_naturalized_citizen_hispanic =
pct(total_naturalized_citizen_hispanic, total_employees),
pct_non_citizen_non_hispanic =
pct(total_non_citizen_non_hispanic, total_employees),
pct_non_citizen_hispanic =
pct(total_non_citizen_hispanic, total_employees),
pct_native_citizen =
pct(total_native_citizen, total_employees),
pct_total_foreign =
pct(total_foreign, total_employees),
pct_naturalized_citizen =
pct(total_naturalized_citizen, total_employees),
pct_non_citizen =
pct(total_non_citizen, total_employees)
)
}
ipums_food_production_selected_occupations_mapped
Calculate the national-level citizenship and ethnicity data for these industry-occupation pairs
ipums_food_production_by_industry_occupation <-
ipums_food_production_selected_occupations_mapped %>%
group_by(industry_code,
industry,
occupation_code,
occupation,
citizenship,
ethnicity) %>%
summarize(num_employees = sum(PERWT)) %>%
pivot_wider(
id_cols = c(industry_code, industry, occupation_code, occupation),
names_from = c(citizenship, ethnicity),
values_from = num_employees,
names_prefix = "total_"
) %>%
replace(is.na(.), 0) %>%
calc_percents()
ipums_food_production_by_industry_occupation
Export the data
write_csv(
ipums_food_production_by_industry_occupation,
"data/exported/ipums_food_production_by_industry_occupation.csv"
)
Group the data by PUMA
ipums_food_production_by_puma <-
ipums_food_production_selected_occupations_mapped %>%
group_by(state_code,
state,
puma_code,
puma,
citizenship,
ethnicity) %>%
summarize(num_employees = sum(PERWT, na.rm = TRUE)) %>%
pivot_wider(
id_cols = c(state_code, state, puma_code, puma),
names_from = c(citizenship, ethnicity),
values_from = num_employees,
names_prefix = "total_"
) %>%
replace(is.na(.), 0) %>%
calc_percents()
ipums_food_production_by_puma %>% select(puma_code,total_foreign)
Export the data
write_csv(
ipums_food_production_by_puma,
"data/exported/ipums_food_production_by_puma.csv"
)
Group the data by PUMA and industry and occupation
ipums_food_production_by_puma_ind_occ <-
ipums_food_production_selected_occupations_mapped %>%
group_by(
state_code,
state,
puma_code,
puma,
industry_code,
industry,
occupation_code,
occupation,
citizenship,
ethnicity
) %>%
summarize(num_employees = sum(PERWT)) %>%
pivot_wider(
id_cols = c(
state_code,
state,
puma_code,
puma,
industry_code,
industry,
occupation_code,
occupation
),
names_from = c(citizenship, ethnicity),
values_from = num_employees,
names_prefix = "total_"
) %>%
replace(is.na(.), 0) %>%
calc_percents()
Export the data
write_csv(
ipums_food_production_by_puma_ind_occ,
"data/exported/ipums_food_production_by_puma_ind_occ.csv"
)
ipums_food_production_by_puma
Join the employment data to the crosswalk data frame
ipums_food_production_by_puma
ipums_food_production_by_puma_crosswalked_to_county <-
ipums_food_production_by_puma %>%
left_join(crosswalk, by = "puma_code") %>%
mutate(
total_employees = round(
total_employees * allocation_factor,
digits = 0
),
total_native_citizen_non_hispanic = round(total_native_citizen_non_hispanic *
allocation_factor, digits = 0),
total_native_citizen_hispanic = round(total_native_citizen_hispanic *
allocation_factor, digits = 0),
total_foreign_non_hispanic = round(total_foreign_non_hispanic *
allocation_factor, digits = 0),
total_foreign_hispanic = round(total_foreign_hispanic *
allocation_factor, digits = 0),
total_naturalized_citizen_non_hispanic = round(total_naturalized_citizen_non_hispanic
* allocation_factor, digits = 0),
total_naturalized_citizen_hispanic = round(total_naturalized_citizen_hispanic
* allocation_factor, digits = 0),
total_non_citizen_non_hispanic = round(total_non_citizen_non_hispanic
* allocation_factor, digits = 0),
total_non_citizen_hispanic = round(total_non_citizen_hispanic
* allocation_factor, digits = 0),
total_native_citizen = total_native_citizen_non_hispanic + total_native_citizen_hispanic,
total_foreign = total_foreign_non_hispanic + total_foreign_hispanic,
total_naturalized_citizen = total_naturalized_citizen_non_hispanic +
total_naturalized_citizen_hispanic,
total_non_citizen = total_non_citizen_non_hispanic + total_non_citizen_hispanic
) %>%
select(1,
state = state.x,
3,
puma = puma.x,
32:33,
35:40,
5:17)
ipums_food_production_by_puma_crosswalked_to_county
ipums_food_production_by_puma_crosswalked_to_county %>% filter(county_code == "06053") %>% select(allocation_factor, total_employees, total_foreign, total_non_citizen)
Join the employment data to the crosswalk data frame
ipums_food_production_by_puma_ind_occ_crosswalked_to_county <-
ipums_food_production_by_puma_ind_occ %>%
left_join(crosswalk, by = "puma_code") %>%
mutate(
total_employees = round(
total_employees * allocation_factor,
digits = 0
),
total_native_citizen_non_hispanic = round(total_native_citizen_non_hispanic
* allocation_factor, digits = 0),
total_native_citizen_hispanic = round(total_native_citizen_hispanic
* allocation_factor, digits = 0),
total_foreign_non_hispanic = round(total_foreign_non_hispanic
* allocation_factor, digits = 0),
total_foreign_hispanic = round(total_foreign_hispanic
* allocation_factor, digits = 0),
total_naturalized_citizen_non_hispanic = round(total_naturalized_citizen_non_hispanic
* allocation_factor, digits = 0),
total_naturalized_citizen_hispanic = round(total_naturalized_citizen_hispanic
* allocation_factor, digits = 0),
total_non_citizen_non_hispanic = round(total_non_citizen_non_hispanic
* allocation_factor, digits = 0),
total_non_citizen_hispanic = round(total_non_citizen_hispanic
* allocation_factor, digits = 0),
total_native_citizen = total_native_citizen_non_hispanic + total_native_citizen_hispanic,
total_foreign = total_foreign_non_hispanic + total_foreign_hispanic,
total_naturalized_citizen = total_naturalized_citizen_non_hispanic +
total_naturalized_citizen_hispanic,
total_non_citizen = total_non_citizen_non_hispanic + total_non_citizen_hispanic
) %>%
select(1,
state = state.x,
3,
puma = puma.x,
36:37,
5:8,
39:44,
9:21)
ipums_food_production_by_puma_crosswalked_to_county
Group the data by county
# This step is required to deal with situations
# where multiple PUMAs split a single county
ipums_food_production_by_county <-
ipums_food_production_by_puma_crosswalked_to_county %>%
group_by(state_code, state, county_code, county) %>%
calc_percents()
Export the data
write_csv(
ipums_food_production_by_county,
"data/exported/ipums_food_production_by_county.csv"
)
Group the data by industry and occupation and county
ipums_food_production_by_puma_ind_occ_crosswalked_to_county
# This step is required to deal with situations
# where multiple PUMAs split a single county
ipums_food_production_by_county_ind_occ <-
ipums_food_production_by_puma_ind_occ_crosswalked_to_county %>%
group_by(
state_code,
state,
county_code,
county,
industry_code,
industry,
occupation_code,
occupation
) %>%
calc_percents()
ipums_food_production_by_county_ind_occ
Export the data
write_csv(
ipums_food_production_by_county_ind_occ,
"data/exported/ipums_food_production_by_county_ind_occ.csv"
)
Analyze the data
What is the total number of workers employed in these industries?
sum(ipums_food_production_by_puma$total_employees)
What is the total number of foreigners employed in these industries?
sum(ipums_food_production_by_puma$total_foreign)
What’s that as a percent?
sum(ipums_food_production_by_puma$total_foreign) /
sum(ipums_food_production_by_puma$total_employees) * 100
How many of the jobs have a higher proportion of foreigners employed than the national average of 17.1%?
ipums_food_production_by_industry_occupation %>%
filter(pct_total_foreign > 17.1) %>%
select(
industry,
occupation,
total_employees,
pct_total_foreign,
pct_total_foreign_hispanic
) %>%
arrange(desc(total_employees))
What is the total number of Hispanic foreigners employed in these industries?
sum(ipums_food_production_by_puma$total_foreign_hispanic)
What’s that as a percent of all foreign workers in these industries?
sum(ipums_food_production_by_puma$total_foreign_hispanic) /
sum(ipums_food_production_by_puma$total_foreign) * 100
Which PUMAs have the highest number of immigrant foodworkers?
ipums_food_production_by_puma %>%
arrange(desc(total_foreign)) %>%
select(
state,
puma,
total_employment_selected_industries,
total_foreign,
total_foreign_hispanic
) %>%
head(23) # 2,341 PUMAs / 100 = 23.41
And how many immigrant workers are in these PUMAs?
ipums_food_production_by_puma %>%
arrange(desc(total_foreign)) %>%
select(puma,
total_employment_selected_industries,
total_foreign,
total_foreign_hispanic) %>%
head(23) %>%
ungroup() %>%
summarize(sum(total_foreign))
And how many of these foreign-born workers are Hispanic?
ipums_food_production_by_puma %>%
arrange(desc(total_foreign)) %>%
select(puma,
total_employment_selected_industries,
total_foreign,
total_foreign_hispanic) %>%
head(23) %>%
ungroup() %>%
summarize(sum(total_foreign_hispanic))
Which counties have the highest number of immigrant foodworkers?
ipums_food_production_by_county %>%
arrange(desc(total_foreign)) %>%
select(
state,
county,
total_employment_selected_industries,
total_foreign,
total_foreign_hispanic
) %>%
head(31) # 3,142 counties / 100 = 31.42
And how many immigrant workers are in these counties?
ipums_food_production_by_county %>%
arrange(desc(total_foreign)) %>%
select(
county,
total_employment_selected_industries,
total_foreign,
total_foreign_hispanic
) %>%
head(31) %>%
ungroup() %>%
summarize(sum(total_foreign))
And how many of these foreign-born workers are Hispanic?
ipums_food_production_by_county %>%
arrange(desc(total_foreign)) %>%
select(
county,
total_employment_selected_industries,
total_foreign,
total_foreign_hispanic
) %>%
head(31) %>%
ungroup() %>%
summarize(sum(total_foreign_hispanic))
What is the distribution of foreign workers in these industries by state?
foodworkers_by_state <- ipums_food_production_by_puma %>%
group_by(state) %>%
summarize(
state_total_employment_selected_industries =
sum(total_employees),
state_total_foreign = sum(total_foreign),
state_pct_total_foreign = round(
sum(total_foreign) / sum(total_employees) * 100
),
2,
state_total_foreign_hispanic = sum(total_foreign_hispanic),
state_pct_total_foreign_hispanic = round(
sum(total_foreign_hispanic) /
sum(total_employees) * 100
),
2
) %>%
mutate(
rank_total_employment = dense_rank(desc(
state_total_employment_selected_industries
)),
rank_total_foreign = dense_rank(desc(state_total_foreign)),
rank_total_foreign_hispanic = dense_rank(desc(state_total_foreign_hispanic))
) %>%
select(
state,
state_total_employment_selected_industries,
state_total_foreign,
state_pct_total_foreign,
state_total_foreign_hispanic,
state_pct_total_foreign_hispanic,
rank_total_employment,
rank_total_foreign,
rank_total_foreign_hispanic
) %>%
arrange(desc(state_total_employment_selected_industries))
foodworkers_by_state
What proportion of total foodworker employment do the top 10 states account for?
foodworkers_by_state %>%
arrange(desc(state_total_employment_selected_industries)) %>%
head(10) %>%
summarize(sum(state_total_employment_selected_industries))
What is the distribution of foreign workers in these industries by state?
foodworkers_by_state %>%
summarize(sum(state_total_employment_selected_industries))
Where do the “Red Zone” states stand?
red_zone <-
data.frame(
"state" = c(
"Alabama/AL",
"Arizona/AZ",
"Arkansas/AR",
"California/CA",
"Florida/FL",
"Georgia/GA",
"Idaho/ID",
"Iowa/IA",
"Kansas/KS",
"Louisiana/LA",
"Mississippi/MS",
"Missouri/MO",
"Nevada/NV",
"North Carolina/NC",
"North Dakota/ND",
"Oklahoma/OK",
"South Carolina/SC",
"Tennessee/TN",
"Texas/TX",
"Utah/UT",
"Wisconsin/WI"
),
"red_zone" = TRUE
)
red_zone
Join with the red zone states data frame
foodworkers_by_state <- foodworkers_by_state %>%
left_join(red_zone, by = "state")
Where do the Red Zone states stand?
foodworkers_by_state %>%
filter(red_zone == TRUE)
Export the states
write_csv(foodworkers_by_state,
"data/exported/foodworkers_by_state.csv")
---
title: "covid-immigrant-foodworkers"
author: "Joe Yerardi"
date: "5/7/2020"
output:
  html_notebook:
    toc: true
    toc_float: true
---

```{r setup, include = F}
# We don't want to see any warnings from our code
knitr::opts_chunk$set(warning = F)

# We don't want to see any messages
knitr::opts_chunk$set(message = F)

# Set key file for the Census Bureau API key
key_file <- "key_file.txt"
```

```{r, include = F}
library(ipumsr)
library(labelled)
library(purrr)
library(rgdal)
library(sf)
library(tidycensus)
library(tidyverse)
library(tigris)
```

# Import and format data

## Import IPUMS

```{r, echo=FALSE, message=FALSE}
# IPUMS (2014-18 ACS; population = 16 and older; employed - at work,
# employed - not at work)
ipums <- read.table("data/usa_00015.dat")
ddi <- read_ipums_ddi("data/usa_00015.xml")
ipums <- read_ipums_micro(ddi) %>%
  mutate(
    state_code = as_factor(US2018C_ST, levels = "values"),
    state = as_factor(US2018C_ST, levels = "labels"),
    puma_code = paste0(state_code, as_factor(US2018C_PUMA)),
    industry_code = as_factor(US2018C_INDP, levels = "values"),
    industry = as_factor(US2018C_INDP, levels = "labels"),
    occupation_code =  as_factor(US2018C_OCCP, levels = "values"),
    occupation = as_factor(US2018C_OCCP, levels = "labels"),
    citizenship_code = as_factor(CITIZEN, levels = "values"),
    citizenship = as_factor(CITIZEN, levels = "labels"),
    ethnicity_code = as_factor(HISPAN, levels = "values"),
    ethnicity = as_factor(HISPAN, levels = "labels")
  ) %>%
  select(16:26, 5)

ipums
```

## PUMA shapefiles

```{r, echo=FALSE, message=FALSE}
pumas <- readOGR("data/ipums_puma_2010/ipums_puma_2010.shp") %>%
  # Convert shapefile to sf object
  st_as_sf(crs = 5070, remove = F) %>%
  select(puma_code = GEOID, puma = Name, geometry)
```

## County shapefiles

```{r, echo=FALSE, message=FALSE}
counties <-
  readOGR("data/tl_2019_us_county/tl_2019_us_county.shp") %>%
  # Convert shapefile to sf object
  st_as_sf(crs = 5070, remove = F) %>%
  select(county_code = GEOID, county = NAMELSAD, geometry)
```

## COVID county cases and deaths

```{r}
covid <- read_csv("data/us-counties.csv",
                  col_types = cols(.default = "?", date = "D")) %>%
  rename(county_code = fips) %>%
  # Filter to the latest data for each county
  group_by(county_code) %>%
  slice(which.max(date))

covid
```

## County populations

### Read-in Census API key

```{r}
census_key <- (readLines(key_file)[1])
```

### Create list of all US states to iterate through in the API calls

```{r}
us <- unique(fips_codes$state)[1:51]

us
```

### Download population data from the 2014-2018 ACS

```{r, echo=FALSE, message=FALSE}
population <- map_dfr(us, function(x) {
  get_acs(
    geography = "county",
    variables = "B01001_001",
    state = x,
    key = census_key
  )
}) %>%
  mutate(income_quartile = ntile(estimate, 4)) %>%
  select(county_code = GEOID,
         county = NAME,
         population = estimate)

population
```

## PUMA-to-county crosswalk

```{r}
county_to_puma_crosswalk <- read_csv("data/geocorr2018.csv", skip = 1) %>%
  mutate(puma_code = paste0(`State code`, `PUMA (2012)`)) %>%
  select(
    puma_code,
    county_code = `County code (2014)`,
    state = `State abbreviation`,
    county = `2014 county name`,
    puma = `PUMA12 name`,
    allocation_factor = `puma12 to county14 allocation factor`
  )

county_to_puma_crosswalk
```

## Join the COVID, population and crosswalk data frames

```{r}
crosswalk <- list(covid, population, county_to_puma_crosswalk) %>%
  reduce(full_join, by = "county_code") %>%
  mutate(
    puma = paste(puma, state.x, sep = ", "),
    cases_per_100k = round(cases / population * 100000, digits = 0),
    deaths_per_100k = round(deaths / population * 100000, digits = 0)
  ) %>%
  select(
    1,
    state = state.x,
    county_code,
    county = county.y,
    puma_code,
    puma,
    population,
    cases,
    deaths,
    cases_per_100k,
    deaths_per_100k,
    allocation_factor
  )

crosswalk
```

## Filter to the food production industries we're interested in

```{r}
food_production_industries <-
  list("0170", # Crop production
       "0180", # Animal production and aquaculture
       "0290", # Support activities for agriculture and forestry
       "1070", # Animal food, grain and oilseed milling
       "1080", # Sugar and confectionery products
       "1090", # Fruit and vegetable preserving and specialty food manufacturing
       "1170", # Dairy product manufacturing
       "1180", # Animal slaughtering and processing
       "1270", # Bakeries and tortilla manufacturing, except retail bakeries
       "1280", # Seafood and other miscellaneous foods; n.e.c.
       "1290") # Not specified food industries

ipums_food_production <- ipums %>%
  filter(industry_code %in% food_production_industries)
```

## Calculate employment by industry-occupation grouping

```{r}
ipums_food_production_employment_by_ind_occ <-
  ipums_food_production %>%
  group_by(industry, occupation) %>%
  summarize(num_employees = sum(PERWT)) %>%
  pivot_wider(
    id_cols = c(industry, occupation),
    values_from = num_employees,
    names_prefix = "num_employees"
  ) %>%
  replace(is.na(.), 0) %>%
  # Exclude from our analysis ind-occ groupings
  # with fewer than 2,500 employees nationally
  filter(num_employees >= 2500)
```

## Export the data

```{r}
write_csv(
  ipums_food_production_employment_by_ind_occ,
  "data/exported/ipums_food_production_employment_by_ind_occ.csv"
)
```

## Filter the industry-occupation pairs

### Import data frame of food production industry-occupation pairs

```{r}
ipums_food_production_employment_by_ind_occ <-
  read_csv("data/ipums_food_production_total_employment_by_ind_occ.csv")
```

### Join the industry-occupation pairs to the ipums data and filter to just the industry-occupation pairs we want

```{r}
ipums_food_production_selected_occupations <-
  ipums_food_production %>%
  inner_join(ipums_food_production_employment_by_ind_occ,
             by = c("industry", "occupation")) %>%
  filter(exclude == FALSE) %>%
  select(-num_employees, -exclude)

ipums_food_production_selected_occupations
```

## And join with the pumas data frame to get the PUMA names

```{r}
ipums_food_production_selected_occupations <-
  ipums_food_production_selected_occupations %>%
  left_join(pumas, by = "puma_code") %>%
  mutate(puma = paste(puma, state, sep = ", ")) %>%
  select(1:3,
         13,
         4:12)

ipums_food_production_selected_occupations
```

```{r}
citizenship_lookup = tibble(
  citizenship_code = factor(c(
    0, 1, 2, 3, 4, 5
  )),
  citizenship = c(
    "native_citizen", # n/a
    "native_citizen", # born_abroad_citizen
    "naturalized_citizen", # naturalized_citizen
    "non_citizen", # non_citizen
    "non_citizen_with_papers", # non_citizen_with_papers
    "foreign_born_status_unknown"
  )
)

ethnicity_lookup = tibble(
  ethnicity_code = factor(c(
    0, 1, 2, 3, 4, 9
  )),
  ethnicity = c(
    "non_hispanic",
    "hispanic", # mexican
    "hispanic", # puerto_rican
    "hispanic", # cuban
    "hispanic", # other
    "not_reported"
  )
)

ipums_food_production_selected_occupations_mapped <-
  ipums_food_production_selected_occupations %>%
  select(-ethnicity,-citizenship) %>%
  left_join(ethnicity_lookup, by="ethnicity_code") %>%
  left_join(citizenship_lookup, by="citizenship_code")
```

```{r}
pct <- function (part,whole) {
  round(part / whole * 100, digits = 0)
}
```


```{r}
calc_percents <- function (rows) {
  rows %>%
  mutate(
    total_native_citizen = (
      total_native_citizen_non_hispanic +
      total_native_citizen_hispanic
    ),
    total_naturalized_citizen = (
      total_naturalized_citizen_non_hispanic +
      total_naturalized_citizen_hispanic
    ),
    total_non_citizen = (
      total_non_citizen_non_hispanic +
      total_non_citizen_hispanic
    ),
    total_foreign_non_hispanic = (
      total_naturalized_citizen_non_hispanic +
      total_non_citizen_non_hispanic
    ),
    total_foreign_hispanic = (
      total_naturalized_citizen_hispanic +
      total_non_citizen_hispanic
    ),
    total_foreign = (
      total_foreign_non_hispanic +
      total_foreign_hispanic
    ),
    total_employees = (
        total_native_citizen +
        total_naturalized_citizen +
        total_non_citizen
    ),
    pct_native_citizen_non_hispanic = 
      pct(total_native_citizen_non_hispanic, total_employees),
    pct_native_citizen_hispanic =
      pct(total_native_citizen_hispanic, total_employees),
    pct_total_foreign_non_hispanic =
      pct(total_foreign_non_hispanic, total_employees),
    pct_total_foreign_hispanic =
      pct(total_foreign_hispanic, total_employees),
    pct_naturalized_citizen_non_hispanic =
      pct(total_naturalized_citizen_non_hispanic, total_employees),
    pct_naturalized_citizen_hispanic =
      pct(total_naturalized_citizen_hispanic, total_employees),
    pct_non_citizen_non_hispanic =
      pct(total_non_citizen_non_hispanic, total_employees),
    pct_non_citizen_hispanic =
      pct(total_non_citizen_hispanic, total_employees),
    pct_native_citizen =
      pct(total_native_citizen, total_employees),
    pct_total_foreign =
      pct(total_foreign, total_employees),
    pct_naturalized_citizen =
      pct(total_naturalized_citizen, total_employees),
    pct_non_citizen =
      pct(total_non_citizen, total_employees)
  )
}
```


```{r}
ipums_food_production_selected_occupations_mapped
```

## Calculate the national-level citizenship and ethnicity data for these industry-occupation pairs

```{r}
ipums_food_production_by_industry_occupation <-
  ipums_food_production_selected_occupations_mapped %>%
  group_by(industry_code,
           industry,
           occupation_code,
           occupation,
           citizenship,
           ethnicity) %>%
  summarize(num_employees = sum(PERWT)) %>%
  pivot_wider(
    id_cols = c(industry_code, industry, occupation_code, occupation),
    names_from = c(citizenship, ethnicity),
    values_from = num_employees,
    names_prefix = "total_"
  ) %>%
  replace(is.na(.), 0) %>%
  calc_percents()
```

```{r}
ipums_food_production_by_industry_occupation
```

### Export the data

```{r}
write_csv(
  ipums_food_production_by_industry_occupation,
  "data/exported/ipums_food_production_by_industry_occupation.csv"
)
```

### Group the data by PUMA

```{r}
ipums_food_production_by_puma <-
  ipums_food_production_selected_occupations_mapped %>%
  group_by(state_code,
           state,
           puma_code,
           puma,
           citizenship,
           ethnicity) %>%
  summarize(num_employees = sum(PERWT, na.rm = TRUE)) %>%
  pivot_wider(
    id_cols = c(state_code, state, puma_code, puma),
    names_from = c(citizenship, ethnicity),
    values_from = num_employees,
    names_prefix = "total_"
  ) %>%
  replace(is.na(.), 0) %>%
  calc_percents()
```

```{r}
ipums_food_production_by_puma %>% select(puma_code,total_foreign)
```

### Export the data

```{r}
write_csv(
  ipums_food_production_by_puma,
  "data/exported/ipums_food_production_by_puma.csv"
)
```

## Group the data by PUMA and industry and occupation

```{r}
ipums_food_production_by_puma_ind_occ <-
  ipums_food_production_selected_occupations_mapped %>%
  group_by(
    state_code,
    state,
    puma_code,
    puma,
    industry_code,
    industry,
    occupation_code,
    occupation,
    citizenship,
    ethnicity
  ) %>%
  summarize(num_employees = sum(PERWT)) %>%
  pivot_wider(
    id_cols = c(
      state_code,
      state,
      puma_code,
      puma,
      industry_code,
      industry,
      occupation_code,
      occupation
    ),
    names_from = c(citizenship, ethnicity),
    values_from = num_employees,
    names_prefix = "total_"
  ) %>%
  replace(is.na(.), 0) %>%
  calc_percents()
```

### Export the data

```{r}
write_csv(
  ipums_food_production_by_puma_ind_occ,
  "data/exported/ipums_food_production_by_puma_ind_occ.csv"
)
```

```{r}
ipums_food_production_by_puma
```

## Join the employment data to the crosswalk data frame

```{r}
ipums_food_production_by_puma
```

```{r crosswalk the employment data from PUMAs to counties}
ipums_food_production_by_puma_crosswalked_to_county <-
  ipums_food_production_by_puma %>%
  left_join(crosswalk, by = "puma_code") %>%
  mutate(
    total_employees = round(
      total_employees * allocation_factor,
      digits = 0
    ),
    total_native_citizen_non_hispanic = round(total_native_citizen_non_hispanic *
                                          allocation_factor, digits = 0),
    total_native_citizen_hispanic = round(total_native_citizen_hispanic *
                                      allocation_factor, digits = 0),
    total_foreign_non_hispanic = round(total_foreign_non_hispanic *
                                         allocation_factor, digits = 0),
    total_foreign_hispanic = round(total_foreign_hispanic *
                                     allocation_factor, digits = 0),
    total_naturalized_citizen_non_hispanic = round(total_naturalized_citizen_non_hispanic
                                             * allocation_factor, digits = 0),
    total_naturalized_citizen_hispanic = round(total_naturalized_citizen_hispanic
                                         * allocation_factor, digits = 0),
    total_non_citizen_non_hispanic = round(total_non_citizen_non_hispanic
                                     * allocation_factor, digits = 0),
    total_non_citizen_hispanic = round(total_non_citizen_hispanic
                                 * allocation_factor, digits = 0),
    total_native_citizen = total_native_citizen_non_hispanic + total_native_citizen_hispanic,
    total_foreign = total_foreign_non_hispanic + total_foreign_hispanic,
    total_naturalized_citizen = total_naturalized_citizen_non_hispanic +
      total_naturalized_citizen_hispanic,
    total_non_citizen = total_non_citizen_non_hispanic + total_non_citizen_hispanic
  ) %>%
  select(1,
         state = state.x,
         3,
         puma = puma.x,
         32:33,
         35:40,
         5:17)
```

```{r}
ipums_food_production_by_puma_crosswalked_to_county
```
```{r}
ipums_food_production_by_puma_crosswalked_to_county %>% filter(county_code == "06053") %>% select(allocation_factor, total_employees, total_foreign, total_non_citizen)
```

### Join the employment data to the crosswalk data frame
```{r crosswalk the employment data from PUMAs and ind-occ pairs to counties and ind-occ pairs}
ipums_food_production_by_puma_ind_occ_crosswalked_to_county <-
  ipums_food_production_by_puma_ind_occ %>%
  left_join(crosswalk, by = "puma_code") %>%
  mutate(
    total_employees = round(
      total_employees * allocation_factor,
      digits = 0
    ),
    total_native_citizen_non_hispanic = round(total_native_citizen_non_hispanic
                                        * allocation_factor, digits = 0),
    total_native_citizen_hispanic = round(total_native_citizen_hispanic
                                    * allocation_factor, digits = 0),
    total_foreign_non_hispanic = round(total_foreign_non_hispanic
                                       * allocation_factor, digits = 0),
    total_foreign_hispanic = round(total_foreign_hispanic
                                   * allocation_factor, digits = 0),
    total_naturalized_citizen_non_hispanic = round(total_naturalized_citizen_non_hispanic
                                             * allocation_factor, digits = 0),
    total_naturalized_citizen_hispanic = round(total_naturalized_citizen_hispanic
                                         * allocation_factor, digits = 0),
    total_non_citizen_non_hispanic = round(total_non_citizen_non_hispanic
                                     * allocation_factor, digits = 0),
    total_non_citizen_hispanic = round(total_non_citizen_hispanic
                                 * allocation_factor, digits = 0),
    total_native_citizen = total_native_citizen_non_hispanic + total_native_citizen_hispanic,
    total_foreign = total_foreign_non_hispanic + total_foreign_hispanic,
    total_naturalized_citizen = total_naturalized_citizen_non_hispanic +
      total_naturalized_citizen_hispanic,
    total_non_citizen = total_non_citizen_non_hispanic + total_non_citizen_hispanic
  ) %>%
  select(1,
         state = state.x,
         3,
         puma = puma.x,
         36:37,
         5:8,
         39:44,
         9:21)
```

```{r}
ipums_food_production_by_puma_crosswalked_to_county
```

## Group the data by county

```{r group the data by county}
# This step is required to deal with situations
# where multiple PUMAs split a single county
ipums_food_production_by_county <-
  ipums_food_production_by_puma_crosswalked_to_county %>%
  group_by(state_code, state, county_code, county) %>%
  calc_percents()
```

### Export the data

```{r}
write_csv(
  ipums_food_production_by_county,
  "data/exported/ipums_food_production_by_county.csv"
)
```

## Group the data by industry and occupation and county

```{r}
ipums_food_production_by_puma_ind_occ_crosswalked_to_county
```

```{r group the data by industry and occupation and county}
# This step is required to deal with situations
# where multiple PUMAs split a single county
ipums_food_production_by_county_ind_occ <-
  ipums_food_production_by_puma_ind_occ_crosswalked_to_county %>%
  group_by(
    state_code,
    state,
    county_code,
    county,
    industry_code,
    industry,
    occupation_code,
    occupation
  ) %>%
  calc_percents()
```


```{r}
ipums_food_production_by_county_ind_occ
```

### Export the data
```{r}
write_csv(
  ipums_food_production_by_county_ind_occ,
  "data/exported/ipums_food_production_by_county_ind_occ.csv"
)
```

# Analyze the data

## What is the total number of workers employed in these industries?

```{r analyze employment by total foreign}
sum(ipums_food_production_by_puma$total_employees)
```

## What is the total number of foreigners employed in these industries?

```{r}
sum(ipums_food_production_by_puma$total_foreign)
```

## What's that as a percent?

```{r}
sum(ipums_food_production_by_puma$total_foreign) /
  sum(ipums_food_production_by_puma$total_employees) * 100
```

## How many of the jobs have a higher proportion of foreigners employed than the national average of 17.1%?

```{r}
ipums_food_production_by_industry_occupation %>%
  filter(pct_total_foreign > 17.1) %>%
  select(
    industry,
    occupation,
    total_employees,
    pct_total_foreign,
    pct_total_foreign_hispanic
  ) %>%
  arrange(desc(total_employees))
```

## What is the total number of Hispanic foreigners employed in these industries?

```{r analyze employment by foreign and Hispanic}
sum(ipums_food_production_by_puma$total_foreign_hispanic)
```

## What's that as a percent of all foreign workers in these industries?

```{r}
sum(ipums_food_production_by_puma$total_foreign_hispanic) /
  sum(ipums_food_production_by_puma$total_foreign) * 100
```

## Which PUMAs have the highest number of immigrant foodworkers?

```{r analyze employment by puma}
ipums_food_production_by_puma %>%
  arrange(desc(total_foreign)) %>%
  select(
    state,
    puma,
    total_employment_selected_industries,
    total_foreign,
    total_foreign_hispanic
  ) %>%
  head(23) # 2,341 PUMAs / 100 = 23.41
```

## And how many immigrant workers are in these PUMAs?

```{r}
ipums_food_production_by_puma %>%
  arrange(desc(total_foreign)) %>%
  select(puma,
         total_employment_selected_industries,
         total_foreign,
         total_foreign_hispanic) %>%
  head(23) %>%
  ungroup() %>%
  summarize(sum(total_foreign))
```

## And how many of these foreign-born workers are Hispanic?

```{r}
ipums_food_production_by_puma %>%
  arrange(desc(total_foreign)) %>%
  select(puma,
         total_employment_selected_industries,
         total_foreign,
         total_foreign_hispanic) %>%
  head(23) %>%
  ungroup() %>%
  summarize(sum(total_foreign_hispanic))
```

## Which counties have the highest number of immigrant foodworkers?

```{r analyze employment by county}
ipums_food_production_by_county %>%
  arrange(desc(total_foreign)) %>%
  select(
    state,
    county,
    total_employment_selected_industries,
    total_foreign,
    total_foreign_hispanic
  ) %>%
  head(31) # 3,142 counties / 100 = 31.42
```

## And how many immigrant workers are in these counties?

```{r}
ipums_food_production_by_county %>%
  arrange(desc(total_foreign)) %>%
  select(
    county,
    total_employment_selected_industries,
    total_foreign,
    total_foreign_hispanic
  ) %>%
  head(31) %>%
  ungroup() %>%
  summarize(sum(total_foreign))
```

## And how many of these foreign-born workers are Hispanic?

```{r}
ipums_food_production_by_county %>%
  arrange(desc(total_foreign)) %>%
  select(
    county,
    total_employment_selected_industries,
    total_foreign,
    total_foreign_hispanic
  ) %>%
  head(31) %>%
  ungroup() %>%
  summarize(sum(total_foreign_hispanic))
```

## What is the distribution of foreign workers in these industries by state?

```{r analysis employment and COVID status by state}
foodworkers_by_state <- ipums_food_production_by_puma %>%
  group_by(state) %>%
  summarize(
    state_total_employment_selected_industries =
      sum(total_employees),
    state_total_foreign = sum(total_foreign),
    state_pct_total_foreign = round(
      sum(total_foreign) / sum(total_employees) * 100
    ),
    2,
    state_total_foreign_hispanic = sum(total_foreign_hispanic),
    state_pct_total_foreign_hispanic = round(
      sum(total_foreign_hispanic) /
        sum(total_employees) * 100
    ),
    2
  ) %>%
  mutate(
    rank_total_employment = dense_rank(desc(
      state_total_employment_selected_industries
    )),
    rank_total_foreign = dense_rank(desc(state_total_foreign)),
    rank_total_foreign_hispanic = dense_rank(desc(state_total_foreign_hispanic))
  ) %>%
  select(
    state,
    state_total_employment_selected_industries,
    state_total_foreign,
    state_pct_total_foreign,
    state_total_foreign_hispanic,
    state_pct_total_foreign_hispanic,
    rank_total_employment,
    rank_total_foreign,
    rank_total_foreign_hispanic
  ) %>%
  arrange(desc(state_total_employment_selected_industries))

foodworkers_by_state
```

## What proportion of total foodworker employment do the top 10 states account for?

```{r}
foodworkers_by_state %>%
  arrange(desc(state_total_employment_selected_industries)) %>%
  head(10) %>%
  summarize(sum(state_total_employment_selected_industries))
```

### What is the distribution of foreign workers in these industries by state?

```{r}
foodworkers_by_state %>%
  summarize(sum(state_total_employment_selected_industries))
```

### Where do the "Red Zone" states stand?

``` {r}
red_zone <-
  data.frame(
    "state" = c(
      "Alabama/AL",
      "Arizona/AZ",
      "Arkansas/AR",
      "California/CA",
      "Florida/FL",
      "Georgia/GA",
      "Idaho/ID",
      "Iowa/IA",
      "Kansas/KS",
      "Louisiana/LA",
      "Mississippi/MS",
      "Missouri/MO",
      "Nevada/NV",
      "North Carolina/NC",
      "North Dakota/ND",
      "Oklahoma/OK",
      "South Carolina/SC",
      "Tennessee/TN",
      "Texas/TX",
      "Utah/UT",
      "Wisconsin/WI"
    ),
    "red_zone" = TRUE
  )

red_zone
```

### Join with the red zone states data frame

```{r}
foodworkers_by_state <- foodworkers_by_state %>%
  left_join(red_zone, by = "state")
```

### Where do the Red Zone states stand?

```{r}
foodworkers_by_state %>%
  filter(red_zone == TRUE)
```

## Export the states

```{r}
write_csv(foodworkers_by_state,
          "data/exported/foodworkers_by_state.csv")
```
